The 2nd Workshop on Trustworthy Learning on Graphs (TrustLOG)

Colocated with the Web Conference 2024


Learning on graphs (LOG) is fundamental and essential to a wide range of web applications, including information retrieval, social network analysis, computational chemistry, and intelligent transportation. Researchers in the area have made significant contributions to theories, algorithms, and open-source systems tailored to address diverse learning tasks. State-of-the-art LOG algorithms have demonstrated superior empirical performance in answering what the optimal learning results would be to serve downstream web applications. For instance, what are the most relevant web pages based on user queries? What are the best groupings of online users to form online communities? What items should recommender systems offer to fit user preferences and to stimulate the growth of e-commerce platforms? Despite the substantial progress in developing high-utility LOG algorithms, recent research has raised concerns about the algorithmic trustworthiness in terms of several critical social aspects, including fairness, transparency, privacy, and security. Black-box LOG algorithms are also found to be vulnerable to malicious attacks, biased against individuals from certain demographic groups, or insecure to information leakage. The untrustworthiness would further limit the potential of LOG algorithm to be deployed in high-impact domains such as online banking and digital health. Therefore, it is essential to ask: why are LOG algorithms untrustworthy? And how can we develop and deploy trustworthy LOG algorithms? To answer these questions, it is essential to introduce a paradigm shift, from solely addressing what questions to understanding how and why. Such paradigm shift could benefit a wide range of web technologies, including fair and reliable anomalies, robust webpage ranking, fair information campaign on social media, as well as factual and private share of knowledge on web platform.

There are several key research challenges involved in trustworthy learning on graphs, including:

  • Understanding the theoretical implications of non-IID graph on the classic trustworthy machine learning;
  • Discovering graph-specific measurements and techniques for trustworthy learning;
  • Achieving trustworthy learning on graphs at scale;
  • Accommodating the heterogeneity of graph data;
  • Dealing with dynamically changing and/or temporal graphs.

Building upon the success of its previous edition, this one-day workshop (TrustLOG-WWW'24) aims to bring together researchers and practitioners from different backgrounds to study these key challenges and enhance the trustworthiness of learning on graphs. The workshop will consist of invited talks, discussion panels, contributed talks, contributed posters on a wide variety of methods and applications related to trustworthy learning on graphs. The proposed workshop welcomes contributions in various format, including research papers, benchmarks and datasets, vision papers, position papers, and white papers. The TrustLOG workshop intends to share visions of investigating new approaches at the intersection of trustworthy learning on graphs and real-world applications.

Call for Papers

We invite contributions to the Trustworthy Learning on Graphs (TrustLOG) co-located with The Web Conference 2024 (formerly known as WWW). The workshop will take place in Singapore, May 13 - 14, 2024.

Important Dates

  • Paper submission: February 13, 2024
  • Reviews period: February 15 - February 26, 2024
  • Final notification: March 4, 2024
  • Camera-ready submission: March 11, 2024
  • Workshop dates: May 13 - 14, 2024

Submission Site

We use EasyChair to manage the submission and review. Abstracts and papers can be submitted through the following link: Please select TrustLOG: The Second Workshop on Trustworthy Learning on Graphs to submit to our workshop.


We invite submissions on a broad range of trustworthy learning on graphs. The topics of interest include (but are not limited to):

  • Robustness, explainability, fairness, reliability, safety, and social norm of graph learning
  • Environmental well-being of graph learning methods
  • Risks and limitations of graph learning methods and foundation models (e.g., LLMs, LMMs) on graphs
  • Applications of trustworthy learning on graphs (e.g., recommender system, knowledge graph, social network analysis, drug discovery, material design, etc.)
  • Datasets and benchmarks for trustworthy learning on graphs

Various types of contributions are welcomed, such as (but are not limited to):

  • Extended abstract
  • Research paper
  • Work-in-progress paper
  • Demo paper
  • Visionary papers/white paper
  • Appraisal papers of existing methods or toolboxes
  • Evaluatory papers on assumptions, methods or toolboxes
  • Relevant work that will be or have been published

Submission Guidelines

Anonymity. The review process will be double-blind. The submitted document should omit any author names, affiliations, or other identifying information. This may include, but is not restricted to acknowledgments, self-citations, references to prior work by the author(s), and so on. Please use the third person to identify your own prior work. You may explicitly refer in the paper to organizations that provided datasets, hosted experiments, or deployed solutions and tools.

Formatting Requirements. Submissions must be a single PDF file: 8 (eight) pages as the main paper, with unlimited pages for references and an optional Appendix (that can contain details on reproducibility, proofs, pseudo-code, etc).

Submissions must be in English, in double-column format, and must adhere to the ACM template and format (also available in Overleaf). Word users may use the Word Interim Template and the recommended setting for LaTeX is:

\documentclass\[sigconf, anonymous, review\]{acmart}.

Originality and Concurrent Submissions. Accepted papers at the workshop are optional to be included in the Companion Proceedings of The Web Conference 2024.

  • Opt-in for the Companion Proceedings if accepted: Submissions that are under review or published/accepted to any peer-reviewed conference/journal with published proceedings cannot be submitted. Submissions that have been previously presented orally, as posters or abstracts-only, or in non-archival venues with no formal proceedings, including workshops or PhD symposia without proceedings, are allowed.
  • Opt-out for the Companion Proceedings if accepted: We allow submissions that are under review at or published/accepted to any preprint servers (e.g., arXiv) and/or peer-reviewed conference/journal with published proceedings.

Authors may submit anonymized work that is already available as a preprint (e.g., on arXiv or SSRN) without citing it. The ACM has a strict policy against plagiarism, misrepresentation, and falsification that applies to all publications.

Ethical Use of Data and Informed Consent. Authors are encouraged to include a section on the ethical use of data and/or informed consent of research subjects in their paper, when appropriate. You and your co-authors are subject to all ACM Publications Policies, including ACM's Publications Policy on Research Involving Human Participants and Subjects (posted in 2021). Please ensure all authors are familiar with these policies.

Please consult the regulations of your institution(s) indicating when a review by an Institutional Ethics Review Board (IRB) is needed. Note that submitting your research for approval by such may not always be sufficient. Even if such research has been approved by your IRB, the program committee might raise additional concerns about the ethical implications of the work and include these concerns in its review.

Agenda (Tentative)

Time Event
9:00 AM~9:10 AM Opening Remarks
9:10 AM~9:55 AM Invited Talk Speaker: Dr. Leman Akoglu
9:55 AM~10:40 AM Invited Talk Speaker: Dr. Tina Eliassi-Rad
10:40 AM~10:55 AM Coffee break
10:55 AM~11:40 AM Oral Presentations for Accepted Papers
11:40 AM~12:25 PM Invited Talk Speaker: Dr. Vagelis Papalexakis
12:25 PM~13:30 PM Lunch break
13:30 PM~14:15 PM Tentative Invited Talk Speaker #1
14:15 PM~15:00 PM Tentative Invited Talk Speaker #2
15:00 PM~15:15 PM Coffee break
15:15 PM~16:00 PM Oral Presentations for Accepted Papers
16:00 PM~16:45 PM Invited Talk Speaker: Dr. Xiang Wang
16:45 PM~17:00 PM Award Ceremony and Closing Remarks
17:00 PM~18:00 PM Poster Session

Keynote Speakers

Dr. Leman Akoglu

Dr. Leman Akoglu is the Heinz College Dean's Associate Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. She also holds courtesy appointments at the Machine Learning Department and the Computer Science Department of School of Computer Science. At Heinz, She directs the Data Analytics Techniques Algorithms Lab. Her research interests are broadly in data mining, graph mining, machine learning, and knowledge discovery, with specific focus on anOmaLiEs---identifying and characterizing 'what stands out' in large-scale, time-varying, multi-modal data sources through scalable computational methods.

Dr. Tina Eliassi-Rad

Dr. Tina Eliassi-Rad is the inaugural President Joseph E. Aoun Professor at Northeastern University. She is also a core faculty member at Northeastern's Network Science Institute and the Institute for Experiential AI. In addition, she is an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Center. Her research is at the intersection of data mining, machine learning, and network science. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, drug discovery, democracy and online discourse, and ethics in machine learning. Her algorithms have been incorporated into systems used by governments and industry (e.g., IBM System G Graph Analytics), as well as open-source software (e.g., Stanford Network Analysis Project). Tina served as the program co-chair for KDD 2017, NetSci 2017, and IC2S2 2020. Tina received an Outstanding Mentor Award from the U.S. Department of Energy's Office of Science, was named one of the 100 Brilliant Women in AI Ethics, received Northeastern University's Excellence in Research and Creative Activity Award, and was awarded the Lagrange Prize-CRT Foundation. Tina is a fellow of ISI Foundation and Network Science Society.

Dr. Vagelis Papalexakis

Dr. Vagelis Papalexakisis an Associate Professor at the Computer Science and Engineering Department at the University of California Riverside. He has broad research interests in data mining, machine learning, and data science research. A major focus of his research is tensor decompositions for machine learning and data science. His work has attracted a number of distinctions, including the 2017 SIGKDD Dissertation Award (runner-up), the National Science Foundation CAREER award, the 2021 IEEE DSAA Next Generation Data Scientist Award, and the ICDM 2022 Tao Li Award which awards excellence in early-career researchers.

Dr. Xiang Wang

Dr. Xiang Wangis a Professor in University of Science and Technology of China. His research interests lie in developing trustworthy deep learning and artificial intelligence algorithms with better interpretability, generalization, and robustness. His research is motivated by, and contributes to, graph-structured applications in information retrieval (e.g., personalized recommendation), data mining (e.g., graph pre-training), security (e.g., fraud detection in fintech, information security in system), and multimedia (e.g., video question answering). His work has over 50 publications in top-tier conferences and journals. Over 10 papers have been featured in the most cited and influential list (e.g., KDD 2019, SIGIR 2019, SIGIR 2020, SIGIR 2021) and best paper finalist (e.g., WWW 2021, CVPR 2022).

More to be announced.


Organzing Chairs


Jingrui He

Associate Professor

University of Illinois at Urbana-Champaign


Jian Kang

Assistant Professor

University of Rochester


Fatemeh Nargesian

Assistant Professor

University of Rochester


An Zhang

Research Fellow

National University of Singapore


Dawei Zhou

Assistant Professor
Virginia Tech

Publicity Chair


Haohui Wang

Ph.D. Student

Virginia Tech


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